Time-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics data
Abstract Background The rapid advancement of single-cell RNA sequencing (scRNAseq) technology provides high-resolution views of transcriptomic activity within individual cells. Most routine analyses of scRNAseq data focus on individual genes; however, the one-gene-at-a-time analysis is likely to mis...
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2025-07-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06218-w |
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| author | Shuyi Yang Anderson Bussing Giampiero Marra Michelle L. Brinkmeier Sally A. Camper Shannon W. Davis Yen-Yi Ho |
| author_facet | Shuyi Yang Anderson Bussing Giampiero Marra Michelle L. Brinkmeier Sally A. Camper Shannon W. Davis Yen-Yi Ho |
| author_sort | Shuyi Yang |
| collection | DOAJ |
| description | Abstract Background The rapid advancement of single-cell RNA sequencing (scRNAseq) technology provides high-resolution views of transcriptomic activity within individual cells. Most routine analyses of scRNAseq data focus on individual genes; however, the one-gene-at-a-time analysis is likely to miss meaningful genetic interactions. Gene co-expression analysis addresses this limitation by identifying coordinated changes in gene expression in response to cellular conditions, such as developmental or temporal trajectories. Existing approaches to gene co-expression analysis often assume restrictive linear relationships. However, gene co-expression can change in complex, non-linear ways, which suggests the need for more flexible and accurate methods. Results We propose a copula-based framework, TIME-CoExpress, with proper data-driven smoothing functions to model non-linear changes in gene co-expression along cellular temporal trajectories. Our method provides the flexibility to incorporate characteristics commonly observed in scRNAseq data, such as over-dispersion and zero-inflation, into the modeling framework. In addition to modeling gene co-expression, TIME-CoExpress captures dynamic changes in gene-level zero-inflation rates and mean expression levels, providing a more comprehensive analysis of scRNAseq data. Through a series of simulation analyses, we evaluated the performance of the proposed approach. We further demonstrated its implementation using a scRNAseq dataset and identified differentially co-expressed gene pairs along the cellular temporal trajectory during pituitary embryonic development, comparing $${Nxn}^{-/-}$$ and wild-type mice. Conclusions The proposed framework enables flexible and robust identification of dynamic, non-linear changes in gene co-expression, zero-inflation rates, and mean expression levels along temporal trajectories in scRNAseq data. Detecting these changes provides deeper insights into the biological processes and offers a better understanding of gene regulation throughout cellular development. |
| format | Article |
| id | doaj-art-ca37cf5c2f2b4f3a9061dde2413d4919 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-ca37cf5c2f2b4f3a9061dde2413d49192025-08-20T04:03:11ZengBMCBMC Bioinformatics1471-21052025-07-0126112610.1186/s12859-025-06218-wTime-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics dataShuyi Yang0Anderson Bussing1Giampiero Marra2Michelle L. Brinkmeier3Sally A. Camper4Shannon W. Davis5Yen-Yi Ho6Department of Statistics, University of South CarolinaDepartment of Statistics, University of South CarolinaDepartment of Statistical Science, University College LondonDepartment of Human Genetics, University of MichiganDepartment of Human Genetics, University of MichiganDepartment of Biological Sciences, University of South CarolinaDepartment of Statistics, University of South CarolinaAbstract Background The rapid advancement of single-cell RNA sequencing (scRNAseq) technology provides high-resolution views of transcriptomic activity within individual cells. Most routine analyses of scRNAseq data focus on individual genes; however, the one-gene-at-a-time analysis is likely to miss meaningful genetic interactions. Gene co-expression analysis addresses this limitation by identifying coordinated changes in gene expression in response to cellular conditions, such as developmental or temporal trajectories. Existing approaches to gene co-expression analysis often assume restrictive linear relationships. However, gene co-expression can change in complex, non-linear ways, which suggests the need for more flexible and accurate methods. Results We propose a copula-based framework, TIME-CoExpress, with proper data-driven smoothing functions to model non-linear changes in gene co-expression along cellular temporal trajectories. Our method provides the flexibility to incorporate characteristics commonly observed in scRNAseq data, such as over-dispersion and zero-inflation, into the modeling framework. In addition to modeling gene co-expression, TIME-CoExpress captures dynamic changes in gene-level zero-inflation rates and mean expression levels, providing a more comprehensive analysis of scRNAseq data. Through a series of simulation analyses, we evaluated the performance of the proposed approach. We further demonstrated its implementation using a scRNAseq dataset and identified differentially co-expressed gene pairs along the cellular temporal trajectory during pituitary embryonic development, comparing $${Nxn}^{-/-}$$ and wild-type mice. Conclusions The proposed framework enables flexible and robust identification of dynamic, non-linear changes in gene co-expression, zero-inflation rates, and mean expression levels along temporal trajectories in scRNAseq data. Detecting these changes provides deeper insights into the biological processes and offers a better understanding of gene regulation throughout cellular development.https://doi.org/10.1186/s12859-025-06218-wZero-inflated bivariate count dataSingle-cell RNA sequencingDynamic correlationPseudotimeNon-linear regressionSemiparametric regression |
| spellingShingle | Shuyi Yang Anderson Bussing Giampiero Marra Michelle L. Brinkmeier Sally A. Camper Shannon W. Davis Yen-Yi Ho Time-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics data BMC Bioinformatics Zero-inflated bivariate count data Single-cell RNA sequencing Dynamic correlation Pseudotime Non-linear regression Semiparametric regression |
| title | Time-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics data |
| title_full | Time-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics data |
| title_fullStr | Time-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics data |
| title_full_unstemmed | Time-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics data |
| title_short | Time-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics data |
| title_sort | time coexpress temporal trajectory modeling of dynamic gene co expression patterns using single cell transcriptomics data |
| topic | Zero-inflated bivariate count data Single-cell RNA sequencing Dynamic correlation Pseudotime Non-linear regression Semiparametric regression |
| url | https://doi.org/10.1186/s12859-025-06218-w |
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